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Using Minimum Path Cover to Boost Dynamic Programming on DAGs: Co-linear Chaining Extended

  • Anna Kuosmanen
  • Topi Paavilainen
  • Travis Gagie
  • Rayan Chikhi
  • Alexandru Tomescu
  • Veli Mäkinen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10812)

Abstract

Aligning sequencing reads on graph representations of genomes is an important ingredient of pan-genomics. Such approaches typically find a set of local anchors that indicate plausible matches between substrings of a read to subpaths of the graph. These anchor matches are then combined to form a (semi-local) alignment of the complete read on a subpath. Co-linear chaining is an algorithmically rigorous approach to combine the anchors. It is a well-known approach for the case of two sequences as inputs. Here we extend the approach so that one of the inputs can be a directed acyclic graph (DAGs), e.g. a splicing graph in transcriptomics or a variant graph in pan-genomics.

This extension to DAGs turns out to have a tight connection to the minimum path cover problem, asking us to find a minimum-cardinality set of paths that cover all the nodes of a DAG. We study the case when the size k of a minimum path cover is small, which is often the case in practice. First, we propose an algorithm for finding a minimum path cover of a DAG (VE) in \(O(k|E|\log |V|)\) time, improving all known time-bounds when k is small and the DAG is not too dense. Second, we introduce a general technique for extending dynamic programming (DP) algorithms from sequences to DAGs. This is enabled by our minimum path cover algorithm, and works by mimicking the DP algorithm for sequences on each path of the minimum path cover. This technique generally produces algorithms that are slower than their counterparts on sequences only by a factor k. Our technique can be applied, for example, to the classical longest increasing subsequence and longest common subsequence problems, extended to labeled DAGs. Finally, we apply this technique to the co-linear chaining problem, which is a generalization of both of these two problems. We also implemented the new co-linear chaining approach. Experiments on splicing graphs show that the new method is efficient also in practice.

Notes

Acknowledgements

We thank the anonymous reviewers for comments that improved the presentation of this paper. We thank Gonzalo Navarro for pointing out the connection to pattern matching on hypertexts. This work was funded in part by the Academy of Finland (grant 274977 to AIT and grants 284598 and 309048 to AK and to VM), and by Futurice Oy (to TP).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Anna Kuosmanen
    • 1
  • Topi Paavilainen
    • 1
  • Travis Gagie
    • 2
  • Rayan Chikhi
    • 3
  • Alexandru Tomescu
    • 1
  • Veli Mäkinen
    • 1
  1. 1.Helsinki Institute for Information Technology HIIT, Department of Computer ScienceUniversity of HelsinkiHelsinkiFinland
  2. 2.Diego Portales UniversitySantiagoChile
  3. 3.CNRS, CRIStALUniversity of Lille 1Villeneuve-d’AscqFrance

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